Human action recognition is a quite hugely investigated area where most remarkable action recognition networks usually use large-scale coarse-grained action datasets of daily human actions as inputs to state the superiority of their networks. We intend to recognize our small-scale fine-grained Tai Chi action dataset using neural networks and propose a transfer-learning method using NTU RGB+D dataset to pre-train our network. More specifically, the proposed method first uses a large-scale NTU RGB+D dataset to pre-train the Transformer-based network for action recognition to extract common features among human motion. Then we freeze the network weights except for the fully connected (FC) layer and take our Tai Chi actions as inputs only to train the initialized FC weights. Experimental results show that our general model pipeline can reach a high accuracy of small-scale fine-grained Tai Chi action recognition with even few inputs and demonstrate that our method achieves the state-of-the-art performance compared with previous Tai Chi action recognition methods.
翻译:人类行动识别是一个非常大规模的调查领域,最显著的行动识别网络通常使用大规模粗粗的人类日常行动数据集作为说明其网络优越性的投入。我们打算承认我们使用神经网络的小规模微细的太极行动数据集,并提议使用NTU RGB+D数据集进行转移学习方法,以预培训我们的网络。更具体地说,拟议方法首先使用大规模NTU RGB+D数据集对基于变压器的行动识别网络进行预培训,以提取人类运动的共同特征。然后,我们冻结网络的重量,但完全连接的(FC)层除外,并将我们的太极行动仅作为培训初始化的FC重量的投入。实验结果表明,我们的总模型管道可以达到一个高精度的小规模微小的太极行动识别,投入甚至很少,并表明我们的方法与以前的太极行动识别方法相比,达到了最先进的性能。